基于改进蚁群算法的室内移动机器人路径规划

Path planning of indoor mobile robot based on improved ant colony algorithm

近年来,路径规划作为移动机器人研究领域中的一个核心课题而备受关注。使用传统蚁群算法为移动机器人规划路径时,搜索容易陷入局部最优,并且收敛速度比较慢,针对这一缺陷,借鉴狼群分配原则对蚁群信息素的更新方式加以改进,避免了蚁群算法陷入局部最优,同时加快了算法收敛速度。然后,综合考虑室内移动机器人路径的长度和平滑度等性能指标,提出一种中心节点替换的平滑方法,使蚁群算法所规划出的路径长度缩短,同时去除了路径中的尖锐转角,提高了路径的平滑性。栅格环境下的仿真结果显示,改进后算法可以规划出更加平滑的路径曲线,且路径长度和算法迭代次数有明显减少,证明改进蚁群算法能有效地应用于室内移动机器人路径规划。

In recent years, path planning has attracted much attention as a core issue in the field of mobile robot research. When the traditional ant colony algorithm is used to plan the path for the mobile robot, the search is easy to fall into the local optimum, and the convergence speed is slow. To overcome this defect, the. ant colony pheromone is improved by the wolf group allocation principle, and the ant colony is avoided. The algorithm falls into local optimum and speeds up the convergence of the algorithm. Then, considering the performance indicators such as the length and smoothness of the indoor mobile robot path, a smoothing method for central node replacement is proposed, which shortens the path length planned by the ant colony algorithm and removes sharp corners in the path. Smoothness of the path. The simulation results in the grid environment show that the improved algorithm can plan a smoother path curve ,and the path length and algorithm iteration number are significantly reduced. It is proved that the improved ant colony algorithm can be effectively applied to indoor mobile robot path planning.